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Semi-automatic classification method for mapping the rice-planted areas of Japan using multi-temporal Landsat images

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Remote Sensing Applications: Society and Environment 2018年 10卷 7-17页

作者： Sakamoto, Toshihiro Sprague, David S. Okamoto, Katsuo Ishitsuka, Naoki Institute for Agro-Environmental Sciences NARO Division of Informatics and Inventory 3-1-3 Kannondai Tsukuba 305-8604 Ibaraki Japan

The rice-planted areas of Japan have decreased continually with reduced rice consumption in the country. Remote sensing techniques are indispensable for providing up-to-date, public data for surveying the locations of paddy fields that have been abandoned or converted to upland crop farming. This study proposes a simple method, using multi-temporal Landsat data, for determining the threshold water index to consistently monitor the spatial distribution of the rice-planted area of Japan without using complex interactive manual operations. This method is much simpler compared to previous methods that semi-automatically or automatically determined the threshold water index based on statistical analyses using manually selected training data or the difference in histogram distribution information between flooded and non-flooded pixels. After comparing six water indices, the Normalized Difference Water Index calculated from green and short-wave infrared reflectance performed best when identifying the rice-planted area with the lowest estimation error (RMSE = 203 ha). It was also found that the proposed method had a higher sensitivity to small-size paddy fields than the previous methods, especially when using Landsat data that included many narrow rice-planted areas distributed along the valley regions. The proposed technique enabled us to consistently check the state of utilization of paddy fields across multiple Landsat images covering large regions of Japan. It was confirmed that the proposed method could accurately guide us to paddy fields that had been abandoned or were being used for upland crop farming, which is not possible from other available land use data, such as the National Land Numerical Information issued by the government. © 2018 Elsevier B.V.

关键词： classification algorithm Land use/land cover Phenology Rice crop Water index

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Texture classification using Combination of LBP and GLRLM Features along with KNN and Multiclass SVM classification 2

Texture Classification using Combination of LBP and GLRLM Fe...

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2nd International Conference on Communication Control and Intelligent Systems (CCIS)

作者： Das, Sourajit Jena, Uma Ranjan Veer Surendra Sai Univ Technol Dept Elect & Telecommun Engn Sambalpur India

ISBN: (纸本)9781509032105

The paper presents a unique combination of texture feature extraction techniques which can be used in image texture analysis. Setting the prime objective of classifying different texture images, the Local Binary Pattern (LBP) and a modified form of Gray Level Run Length Matrix (GLRLM) are implemented initially. The next phase involves use of combination of the former two methods to extract improved features. The feature vectors were obtained by defining the features on the transformed images. These texture features are classified using two classification algorithms, KNN and multiclass SVM. The results of above feature extraction techniques with individual classifiers have been compared. The comparison yields that the combination of LBP and GLRLM texture features shows better classification rate than the features obtained from individual feature extraction techniques. Among the classifiers, Support Vector Machine has better classification rate than the Nearest Neighbor approach for the texture classification.

关键词： Image Texture Analysis classification algorithm Feature Extraction Nearest Neighbor Support Vector Machine

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Reverse phase protein array based tumor profiling identifies a biomarker signature for risk classification of hormone receptor-positive breast cancer

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Translational Proteomics 2014年第1期2卷 52-59页

作者： Sonntag, Johanna Bender, Christian Soons, Zita der Heyde, Silvia von König, Rainer Wiemann, Stefan Sinn, Hans-Peter Schneeweiss, Andreas Beißbarth, Tim Korf, Ulrike Division of Molecular Genome Analysis German Cancer Research Center (DKFZ) Heidelberg Germany Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany Network Modeling Leibniz Institute for Natural Product Research and Infection Biology Hans Knöll Institute Jena Jena Germany Department of Medical Statistics University Medical Center Göttingen Göttingen Germany Integrated Research and Treatment Center Center for Sepsis Control and Care (CSCC) Jena University Hospital Jena Germany Department of Pathology University of Heidelberg Heidelberg Germany National Center for Tumor Diseases (NCT) University of Heidelberg Heidelberg Germany TRON - Translational Oncology Johannes Gutenberg University of Mainz Medicine Mainz Germany Department of Knowledge Engineering Maastricht University Maastricht Netherlands

A robust subclassification of luminal breast cancer, the most common molecular subtype of human breast cancer, is crucial for therapy decisions. While a part of patients is at higher risk of recurrence and requires chemo-endocrine treatment, the other part is at lower risk and also poorly responds to chemotherapeutic regimens. To approximate the risk of cancer recurrence, clinical guidelines recommend determining histologic grading and abundance of a cell proliferation marker in tumor specimens. However, this approach assigns an intermediate risk to a substantial number of patients and in addition suffers from a high interobserver variability. Therefore, the aim of our study was to identify a quantitative protein biomarker signature to facilitate risk classification. Reverse phase protein arrays (RPPA) were used to obtain quantitative expression data for 128 breast cancer relevant proteins in a set of hormone receptor-positive tumors (n = 109). Proteomic data for the subset of histologic G1 (n = 14) and G3 (n = 22) samples were used for biomarker discovery serving as surrogates of low and high recurrence risk, respectively. A novel biomarker selection workflow based on combining three different classification methods identified caveolin-1, NDKA, RPS6, and Ki-67 as top candidates. NDKA, RPS6, and Ki-67 were expressed at elevated levels in high risk tumors whereas caveolin-1 was observed as downregulated. The identified biomarker signature was subsequently analyzed using an independent test set (AUC = 0.78). Further evaluation of the identified biomarker panel by Western blot and mRNA profiling confirmed the proteomic signature obtained by RPPA. In conclusion, the biomarker signature introduced supports RPPA as a tool for cancer biomarker discovery. © 2014 The Authors.

关键词： Biomarker classification algorithm Luminal breast cancer Reverse phase protein array Risk classification

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classification for safety-critical car-cyclist scenarios using machine learning 18

Classification for safety-critical car-cyclist scenarios usi...

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18th IEEE International Conference on Intelligent Transportation Systems

作者： Cara, Irene de Gelder, Erwin TNO POB 756 NL-5700 AT Helmond Netherlands

ISBN: (纸本)9781467365963

The number of fatal car-cyclist accidents is increasing. Advanced Driver Assistance Systems (ADAS) can improve the safety of cyclists, but they need to be tested with realistic safety-critical car-cyclist scenarios. In order to store only relevant scenarios, an online classification algorithm is needed. We demonstrate that machine learning techniques can be used to detect and classify those scenarios based on their trajectory data. A dataset consisting of 99 realistic car-cyclist scenarios is gathered using an instrumented vehicle. We achieved a classification accuracy of the gathered data of 87.9%. The execution time of only 45.8 mu s shows that the algorithm is suitable for online purposes.

关键词： automobiles bicycles feature extraction intelligent transportation systems learning (artificial intelligence) pattern classification road accidents ADAS advanced driver assistance system car-cyclist accident classification algorithm machine learning safety-critical car-cyclist scenario Accuracy classification algorithms Hidden Markov models Machine learning algorithms Support vector machines Trajectory Vehicles

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A Hardware/Software Co-design Architecture for Packet classification

A Hardware/Software Co-design Architecture for Packet Classi...

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22nd International Conference on Microelectronics (ICM 2010)

作者： Ahmed, O. Chattha, K. Areibi, S. Univ Guelph Sch Engn Guelph ON N1G 2W1 Canada

ISBN: (纸本)9781612841519

Packet classification involves matching information from a packet's header to a set of rules in a database in order to determine the manner in which the packet should be processed by network processors. The PCIU algorithm [1] is a novel classification algorithm which improves upon previously published techniques in the literature. The main features of the PCIU algorithm are the low pre-processing time and capability of incremental rule update. Using the network processor to implement packet classification would cause saturation even when the best performing packet classification algorithm is used. In this work, we propose a hardware implementation of the PCIU algorithm. Results obtained indicate that the hardware/software co-design approach achieves 4.3x speedup in terms of preprocessing over a pure software implementation and 5.3x speedup for classification.

关键词： PCIU algorithm classification algorithm hardware implementation hardware software codesign architecture hardware-software codesign matching information network processors packet classification pattern classification pattern matching

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Oxides classification with Random Forests 18th

Oxides Classification with Random Forests

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18th International Conference on Intelligent Computing (ICIC)

作者： Xiao, Kai Chen, Baitong Bao, Wenzheng Cheng, Honglin Xuzhou Univ Technol Sch Informat Engn Xuzhou 221018 Jiangsu Peoples R China Xuzhou First Peoples Hosp Xuzhou 221000 Jiangsu Peoples R China

ISBN: (纸本)9783031138294;9783031138287

Oxides are divided into binary and ternary oxides according to different components. Binary oxides are widely used in industrial catalysis, and the development of new energy field of ternary oxides is also very broad. This paper mainly studies the oxide classification method based on machine learning. In order to facilitate calculation, the method of describing oxides is redefined. The prediction results are evaluated according to the accuracy of evaluation index, F1 score, accuracy and recall rate, and the best algorithm is selected. The results show that the random forest has a good classification effect and is of great significance to the identification of oxides.

关键词： Oxide classification algorithm Machine learning Random forests

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Automatic classification and Coding of Low-Level Events in Nuclear Power Plants Based on Machine Learning 24

Automatic Classification and Coding of Low-Level Events in N...

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24th IEEE International Conference on Computer Supported Cooperative Work in Design (IEEE CSCWD)

作者： Zhang, Lian Cai, Hankun Yang, Meng Ying, Qiang Res Inst Nucl Power Operat Operat Technol Res & Evaluat Ctr Wuhan Peoples R China Shaoxing K3i Technol Co Ltd Zhuji Peoples R China

ISBN: (纸本)9781728165974

At present, Experience Feedback Systems have been established in all currently operating and under construction nuclear power plants, and as such, a large number of low-level events have been collected. Due to human and material constraints, these low-level events have not been well utilized. The main problem is that the number of low-level events is so large that it is impossible to code all events manually. In this paper, through the use of seven classification learning algorithms, automatic coding research on low-level events of nuclear power plants is carried out to demonstrate the feasibility of applying a classification learning algorithm to the field of nuclear power text processing. This paper first introduces low-level events in nuclear power plants, low-level event trend coding, and the classification algorithms based on machine learning and deep learning used in this study. Then, comparative experiments are designed and the results of the experiments are analyzed in detail. Finally, based on the results of this study, suggestions for the use of artificial intelligence in the field of nuclear power text processing are proposed. This study provides a reference for the application of machine learning in the field of nuclear power texts processing.

关键词： Nuclear Power Plant Status Report Automatic classification Coding Bert Machine Learning Deep Learning classification algorithm

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Analysis of an automatic method for surface classification using multifrequency multipolarization SAR data

Analysis of an automatic method for surface classification u...

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1995 International Geoscience and Remote Sensing Symposium (IGARSS 95)

作者： BARONTI, S MACELLONI, G PALOSCIA, S PAMPALONI, P SIGISMONDI, S CNR IROEI-50127 FLORENCEITALY

ISBN: (纸本)0780325680

In this paper an algorithm for automatic classification of land surfaces from multifrequency polarimetric SAR data is presented. Discriminations among different classes of agricultural land surfaces have been carried out on the basis of calibrated backscattering coefficients measured for each field. Nine classes of agricultural surfaces have been identified with a very low error rate.

关键词： MULTIFREQUENCY SAR DATA BACKSCATTERING COEFFICIENT classification algorithm

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Prediction of Healthy Blood with Data Mining classification by using Decision Tree, Naive Bayesian and SVM approaches 6

Prediction of Healthy Blood with Data Mining Classification ...

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6th International Conference on Graphic and Image Processing (ICGIP)

作者： Khalilinezhad, Mandieh Minaei, Behrooz Vernazza, Gianni Dellepiane, Silvana Univ Genoa Dept Naval Engn Elect & Telecommun Genoa Italy Iran Univ Sci & Technol Dept Comp Engn Tehran Iran

ISBN: (纸本)9781628415582

Data mining (DM) is the process of discovery knowledge from large databases. Applications of data mining in Blood Transfusion Organizations could be useful for improving the performance of blood donation service. The aim of this research is the prediction of healthiness of blood donors in Blood Transfusion Organization (BTO). For this goal, three famous algorithms such as Decision Tree C4.5, Naive Bayesian classifier, and Support Vector Machine have been chosen and applied to a real database made of 11006 donors. Seven fields such as sex, age, job, education, marital status, type of donor, results of blood tests (doctors' comments and lab results about healthy or unhealthy blood donors) have been selected as input to these algorithms. The results of the three algorithms have been compared and an error cost analysis has been performed. According to this research and the obtained results, the best algorithm with low error cost and high accuracy is SVM. This research helps BTO to realize a model from blood donors in each area in order to predict the healthy blood or unhealthy blood of donors. This research could be useful if used in parallel with laboratory tests to better separate unhealthy blood.

关键词： classification algorithm Blood donor Data mining

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AN APPLICATION OF MULTIPLE SPACE NEAREST NEIGHBOR CLASSIFIER IN LAND COVER classification

AN APPLICATION OF MULTIPLE SPACE NEAREST NEIGHBOR CLASSIFIER...

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IEEE Joint International Geoscience and Remote Sensing Symposium (IGARSS) / 35th Canadian Symposium on Remote Sensing

作者： Martins-Bede, Flavia de Toledo Reis, Mariane Souza Pantaleao, Eliana Dutra, Luciano Sandri, Sandra Brazilian Natl Inst Space Res INPE Sao Jose Dos Campos Brazil

ISBN: (纸本)9781479957750

This work presents a case study in land cover classification using ms-NN, an extension of k-NN classification algorithm. The case study focuses on an area in the Brazilian Amazon region, with data obtained from LANDSAT5 satellite Thematic Mapper (TM) sensor and Advanced Land Observing System satellite (ALOS) Phase Array L-Band Synthetic Aperture Radar (PALSAR), using Fine Beam Dual. The results obtained with ms-NN are compared with k-NN and Support Vector Machine algorithms, considering the use of a single training set, a Monte Carlo procedure for testing and an extensive number of parameterizations for the classification methods. Considering only the best results for each classifier, ms-NN obtained better results than the other methods.

关键词： land cover classification multiple space nearest neighbor classification algorithm SAR and optical data classification

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